AIDER: A model for social accountability in medical education and practice
Bibliographic record
Abstract
BACKGROUND: Social accountability in healthcare requires physicians and medical institutions to direct their research, services and education activities to adequately address health inequities. The need for greater social accountability has been addressed in numerous national and international healthcare reviews of health disparities and medical education. AIM: The aim of this work is to better understand how to identify underserved populations and address their specific needs and also to provide physicians and medical institutions with a means by which to cultivate social accountability. METHODS: The authors reviewed existing literature and prominent models focusing on social accountability, as well as medical education frameworks, and identified the need to engage underserved stakeholders and incorporate education that includes knowledge translation and reciprocity. The AIDER model was developed to satisfy the need in medical education and practice that is not explicitly addressed in previous models. RESULTS: The AIDER model (Assess, Inquire, Deliver, Educate, Respond) is a continuous monitoring process that explicitly incorporates reciprocal education and continuous collaboration with underserved stakeholders. CONCLUSION: This model is an incremental step forward in helping physicians and medical institutions foster a culture of social accountability both in individual practice and throughout the continuum of medical education.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.022 | 0.033 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.004 | 0.009 |
| Scholarly communication | 0.008 | 0.011 |
| Open science | 0.004 | 0.007 |
| Research integrity | 0.005 | 0.003 |
| Insufficient payload (model declined to judge) | 0.015 | 0.003 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".